Summary of From Redundancy to Relevance: Information Flow in Lvlms Across Reasoning Tasks, by Xiaofeng Zhang et al.
From Redundancy to Relevance: Information Flow in LVLMs Across Reasoning Tasks
by Xiaofeng Zhang, Yihao Quan, Chen Shen, Xiaosong Yuan, Shaotian Yan, Liang Xie, Wenxiao Wang, Chaochen Gu, Hao Tang, Jieping Ye
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed method integrates attention analysis with LLaVA-CAM to analyze the reasoning mechanism of Large Vision Language Models (LVLMs). By exploring the information flow from the perspective of visual representation contribution, it is observed that the information tends to converge in shallow layers but diversify in deeper layers. The study validates its hypothesis through comprehensive experiments on visual question answering and image captioning tasks across various LVLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Vision Language Models can do many things, like understand pictures and answer questions about them. But they work in a way that’s hard to understand because it’s all hidden inside the model. To change this, scientists are looking at how the model uses information from pictures to make decisions. They found that the model tends to focus on important parts of the picture early on, but then starts to look at more details later on. This helps us understand how these models work and can help make them better. |
Keywords
» Artificial intelligence » Attention » Image captioning » Question answering
